Media capture device with power saving and encryption features for partitioned neural network

ABSTRACT

A method for power saving and encryption during analysis of media captured by an information capture device using a partitioned neural network includes replicating, by an information capture device, an artificial neural network (ANN) from a computer server to the information capture device. The ANN on the computer server and a replicated ANN, both, include M layers. The method further includes, in response to captured data being input to be processed, partially processing, by the information capture device, the captured data by executing a first k layers using the replicated ANN, wherein only the k layers are selected to execute on the information capture device. The method further includes transmitting, by the information capture device, an output of the k-th layer to the computer server, which partially processes the captured data by executing the remainder of the M layers using the ANN and the output of the k-th layer.

BACKGROUND

The present invention generally relates to computing technology, andmore specifically, to media capture devices and neural networks thatfacilitate the media capture devices to save power and secure data.

Today, several devices such as, phones, tablet computers, wearabledevices, etc. capture and/or create media objects, such as digitalimages, audio, video, etc. With the increasing need for classifyinglarge volumes of captured and/or extracted media, learning models havebecome a common practice for classifying the captured media objects. Thelearning models, such as for example, artificial neural networks (ANN)and/or convolutional neural networks (CNN), are trained with sampledata, i.e. sample media objects and continuously evolve (learn) duringthe process of classifying new (previously unseen) media objects.

SUMMARY

One or more embodiments of the present invention include acomputer-implemented method for power saving and encryption duringanalysis of media captured by an information capture device using apartitioned neural network. The method includes replicating, by aninformation capture device, an artificial neural network (ANN) from acomputer server to the information capture device, wherein the ANN onthe computer server and a replicated ANN on the information capturedevice, both, include M layers. The method further includes, in responseto captured data being input to be processed, partially processing, bythe information capture device, the captured data by executing a first klayers using the replicated ANN, wherein only the k layers are selectedto execute on the information capture device. The method furtherincludes transmitting, by the information capture device, an output ofthe k-th layer to the computer server, which partially processes thecaptured data by executing the remainder of the M layers using the ANNand the output of the k-th layer.

According to one or more embodiments of the present invention, a systemincludes a memory, and one or more processors coupled to the memory,wherein the one or more processors perform a method for power saving andencryption during analysis of media captured by an information capturedevice using a partitioned neural network. The method includesreplicating, by an information capture device, an artificial neuralnetwork (ANN) from a computer server to the information capture device,wherein the ANN on the computer server and a replicated ANN on theinformation capture device, both, include M layers. The method furtherincludes, in response to captured data being input to be processed,partially processing, by the information capture device, the captureddata by executing a first k layers using the replicated ANN, whereinonly the k layers are selected to execute on the information capturedevice. The method further includes transmitting, by the informationcapture device, an output of the k-th layer to the computer server,which partially processes the captured data by executing the remainderof the M layers using the ANN and the output of the k-th layer.

According to one or more embodiments of the present invention, acomputer program product includes a computer readable storage mediumhaving program instructions embodied therewith. The program instructionsare executable by one or more processors to cause the one or moreprocessors to perform a method including operations for power saving andencryption during analysis of media captured by an information capturedevice using a partitioned neural network. The method includesreplicating, by an information capture device, an artificial neuralnetwork (ANN) from a computer server to the information capture device,wherein the ANN on the computer server and a replicated ANN on theinformation capture device, both, include M layers. The method furtherincludes, in response to captured data being input to be processed,partially processing, by the information capture device, the captureddata by executing a first k layers using the replicated ANN, whereinonly the k layers are selected to execute on the information capturedevice. The method further includes transmitting, by the informationcapture device, an output of the k-th layer to the computer server,which partially processes the captured data by executing the remainderof the M layers using the ANN and the output of the k-th layer.

Other embodiments of the present invention implement features of theabove-described method in computer systems and computer programproducts.

Additional technical features and benefits are realized through thetechniques of the present invention. Embodiments and aspects of theinvention are described in detail herein and are considered a part ofthe claimed subject matter. For a better understanding, refer to thedetailed description and to the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The specifics of the exclusive rights described herein are particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The foregoing and other features and advantages ofthe embodiments of the invention are apparent from the followingdetailed description taken in conjunction with the accompanying drawingsin which:

FIG. 1 is a block diagram of a system for power saving and encryptionduring analysis of a media captured by an information capture deviceusing a partitioned neural network according to one or more embodimentsof the present invention;

FIG. 2 is a block diagram of a system for power saving and encryptionduring analysis of a media captured by an information capture deviceusing a partitioned neural network according to one or more embodimentsof the present invention;

FIG. 3 is a flowchart of a method for power saving and encryption duringanalysis of a media captured by an information capture device using apartitioned neural network according to one or more embodiments of thepresent invention;

FIG. 4 depicts a computer system that implements one or more embodimentsof the present invention;

FIG. 5 depicts a cloud computing environment according to one or moreembodiments of the present invention; and

FIG. 6 depicts abstraction model layers according to one or moreembodiments of the present invention.

DETAILED DESCRIPTION

Embodiments of the present invention facilitate a media capture devicewith power saving and encryption features when using a partitionedneural network to process one or more media objects, such as images,audio, video, etc. Nowadays, a large volume of data, such as images,audios, videos, etc. is created by multiple users using edge devices,such as phones, tablets, computers, wearable devices, dash-cameras,voice recorders, security cameras, etc. A technical challenge exists toprocess such a large volume of such data comprising media using deepneural network (DNN) architecture. Here, a “large volume” can entailmillions of images, audio, video and processing and classifying such avolume of data manually is impractical, if not impossible. Accordingly,embodiments of the present invention provide a practical application toclassifying large volume of media being captured by one or moreinformation capture devices. Further, embodiments of the presentinvention improve the operations of the information capture devices byfacilitating the information capture devices to save power and to securedata. Further yet, embodiments of the present invention address thelimited computation resources on the information capture devices byimproving the computational efficiency of the information capturedevices during such media classification tasks.

FIG. 1 depicts a block diagram of a system 100 for processinginformation captured by one or more information capture devicesaccording to one or more embodiments of the present invention. Aninformation capture device 102 (e.g., camera, phone, security camera,tablet computer, voice recorder etc.) captures information as analogsignals that can be stored in one or more digitized files 103. Theanalog signals sensed by an information sensing array 112 of theinformation capture device 102 are digitized by an analog-to-digital(ADC) module 114. The information capture device 102 can further includea processor 116 that can perform one or more digital signal processingoperations on the digitized data, for example, image processing, audioprocessing, video processing, etc. The processor 116 can save thedigitized data as digitized files 103. The digitized files 103 can besaved as electronic files using one or more digital file storageformats. For example, visual information captured by the informationsensing array 112 can be stored using image file formats such as,portable network graphics (PNG), bitmap (BMP), etc. In the case of audiodata that is sensed by the information sensing array 112, the digitizedaudio can be stored using file formats such as, waveform audio fileformat (WAV), free lossless audio codec (FLAC), etc. In case of videobeing captured, the data can be stored using digital file formats suchas video object (VOB), audio video interleave (AVI), MPEG-14, etc.

In one or more embodiments of the present invention, the processor 116can include one or more processing units, such as processor cores etc.The processor 116 can be a microprocessor, a multiprocessor, a digitalsignal processor, a graphics programming unit, a central processingunit, and other such type of a processing unit, or a combinationthereof. The processor 116 can include, or be coupled with memorydevices 117. The processor 116 can perform one or more operations byexecuting one or more computer executable instructions. Suchinstructions may be stored on the memory devices 117. The memory devices117 can store additional information/data that can be used or output bythe processor 116.

Captured data from the digitized files 103 is transferred through acommunication network 104 to a computer server 106 for furtherprocessing, such as the classification of the digitized files 103.

The communication network 104 can be a computer network, such as theInternet, that uses one or more communication protocols, such asEthernet, etc. In one or more embodiments of the present invention, thedigitized files 103 are captured by users 101 using the informationcapture device 102.

The computer server 106 can be a server cluster, or a distributed serverthat provides a cloud-based processing service for the digitized files103 captured by the information capture devices 102. In one or moreembodiments of the present invention, the computer server 106 includesan artificial neural network (ANN) 122. The ANN 122 can be aconvolutional neural network, a feedforward network, recurrent neuralnetwork, multilayer perceptron, or a combination thereof. The ANN 122can be an independent hardware module in one or more embodiments of thepresent invention. Alternatively, or in addition, the ANN 122 can beimplemented using a processor 127 of the computer server 106. The ANN122 includes multiple layers in which output of one layer is used by asubsequent layer until a final output 123 is generated.

In one or more embodiments of the present invention, the processor 126can include one or more processing units, such as processor cores etc.The processor 126 can be a microprocessor, a multiprocessor, a digitalsignal processor, a graphics programming unit, a central processingunit, and other such type of a processing unit, or a combinationthereof. The processor 126 can include, or be coupled with memorydevices 127. The processor 126 can perform one or more operations byexecuting one or more computer executable instructions. Suchinstructions may be stored on the memory devices 127. The memory devices127 can store additional information/data that can be used or output bythe processor 126.

In one or more embodiments of the present invention, the ANN 122 istrained using a training data 124. The training data 124 includespredefined media such as, images, audio, video, etc., that can includelabels and other hints that can train the ANN 122 to analyze thecaptured data from the information capture device 102 during aninference phase and generate ANN output 123. The ANN output 123 caninclude classification of the digitized files 103 into one or morecategories, object detection results of the digitized files 103, andother such image processing/computer vision, and audio processingresults.

In conventional systems, the digitized files 103 are encrypted beforesending the digitized files 103 to the server 106. If the encryption iscompromised (i.e., hacked), the captured data from the digitized files103 can be exposed.

Embodiments of the present invention integrate the neural networkanalysis with the encryption, by splitting the ANN 122 and creating areplica of the ANN 122 on the information capture device 102. In one ormore embodiments, the captured data is first processed through one ormore layers of the ANN 122, then the output of the ANN 122 is sentthrough network 104 to the computer server 106 for further processingwith the rest of layers of the ANN 122.

Alternatively, in other embodiments, the analog signals sensed by theinformation sensing array 112 on the information capture device 102 isfirst connected through one or more layers of the ANN 122. The output ofthe one or more layers of the ANN 122 is sent through network 104 to thecomputer server 106 for further processing with the rest of the layersof the ANN 122.

In embodiments of the present invention, encryption can also be done forthe weights of the one or more intermediate layers of the ANN 122 andthe output of the intermediate layers of the ANN 122. In this way,captured data is not being transferred through the network 104, thusimproving the security of the captured data. Embodiments of theinvention provide improvements to the system 100, and the components ofthe system 100, such as the information capture device 102, the computerserver 106, and to one or more methods of using the system 100 and/orthe components of the system 100, for example, to analyze digitizedfiles 103 captured by the information capture device 102 in a securemanner.

FIG. 2 is a block diagram that depicts improvements of one or morecomponents of the system 100 for power saving and encryption of captureddata for using a partitioned neural network according to one or moreembodiments of the present invention. The depiction illustrates thelayers 202 in the ANN 122. The ANN 122 can include M layers, where M isany integer. Each layer uses the output from a previous layer exceptlayer #1.

The ANN 122 is trained using the training data 124. Such a trainingincludes learning (i.e., configuring, setting up) one or more weightsassociated with each of the layers 202 of the ANN 122. The weights arelearned automatically using one or more training techniques, such assupervised learning, unsupervised learning, or any other learningtechniques for the ANN 122.

The information capture device 102 includes an ANN-replica 204, which isa replica of the ANN 122. The ANN-replica 204 is identical to the ANN122, and includes the same M layers. Further, to make the ANN-replica204 identical to the ANN 122, the weights that are learned by the ANN122 are transmitted to the ANN-replica 204 on the information capturedevice 102. In one or more embodiments of the present invention, theweights are encrypted by an encryption unit 230 of the computer server106. A decryption unit 232 of the information capture device 102decrypts the encrypted weights from the encryption unit 230. Thedecrypted weights outputted by the decryption unit 232 are configured inthe ANN-replica 204.

The information capture device 102 further includes a layer selector 210that selects how many of the M layers from the ANN-replica 204 are to beexecuted on the information capture device for analyzing a digitizedfile 103 that is created by the information capture device 102. Forexample, the layer selector 210 can select that the first k layers(1≤k≤M) of the ANN-replica 204 be executed by the information capturedevice 102 with the digitized file 103 as input. In one or moreembodiments of the present invention, the layer selector 210 determinesthe value of k based on power consumed by the information capture device102 to execute the layers of the ANN-replica 204. In other embodiments,additional or alternative parameters can be used to select the value ofk.

The output of the layer #k from the ANN-replica 204, with the digitizedfile 103 as the input to the replica-ANN 204, is transmitted to thecomputer server 106. In one or more embodiments of the presentinvention, the output of layer #k is encrypted by an encryption unit 220of the information capture device 102 prior to the transmission. Adecryption unit 222 of the computer server 106 decrypts the output ofthe layer #k. This received output of layer #k is input to the layer#(k+1) of the ANN 122. In one or more embodiments of the presentinvention, a layer locator 212 of the computer server 104 identifies thelayer #(k+1) in the ANN 122 and inputs the received output of layer #kto that layer #(k+1) in the ANN 122.

In one or more embodiments of the present invention, the layer selector210 transmits, to the layer locator 212, the identity of the layer forwhich the output is being sent, i.e., layer #k. The identity of thelayer #k is encrypted by the encryption unit 220 prior to transmission,in one or more embodiments of the present invention. The decryption unit222 decrypts the identity of the layer #k, for use by the layer locator212.

The layers (k+1) to M of the ANN 122 are subsequently executed togenerate the result 123 of the ANN 122. In one or more embodiments ofthe present invention, the result 123 is transmitted to the informationcapture device 102, or to any other device (not shown).

The system 100, accordingly, facilitates a variable division of workloadin which a subset of the layers of the ANN are executed on theinformation capture device 102 and the rest of the layers are executedon the computer server 104. Further, the data that is exchanged betweenthe information capture device 102 and the computer server 104 issecured and even then only intermediate data is exchanged to limitexposure of the entire digitized file 103, and in turn to limitpossibility of the digitized file 103 being hacked during such exchangeof data.

In one or more embodiments of the present invention, the informationcapture device 102 transmits output of each of the layers that areexecuted by the ANN-replica 204, i.e., layers 1-k, along with theidentity of the layer #k.

In one or more embodiments of the present invention, the ANN-replica 204uses the analog signals that are captured by the information sensingarray 112, prior to the captured data being converted into the digitizedfile 103. This facilitates securing the captured data further from beingcompromised. In this case, the ANN 122 is trained using training data124 that includes analog signals.

FIG. 3 depicts a flowchart of a method 300 of analyzing captured datawith power saving and encryption using a partitioned neural networkaccording to one or more embodiments of the present invention. Themethod 300 includes training the ANN 122 of the computer server 106using the training data 124, at block 302. The training can includesupervised learning, unsupervised learning, or any other type of neuralnetwork training. The training data 124 can include analog signalscaptured by information sensing arrays such as the information sensingarray 112. Alternatively, or in addition, the training data 124 caninclude media that are obtained after digitizing such analog signals.The training facilitates the M layers 202 of the ANN 122 to beconfigured with weights. Here, “weight” is a parameter within the ANN122 that transforms input data that is provided to any of the M layers202. Each of the M layers 202 can include multiple weights. The ANN 122is trained to analyze the captured data, either in the form of analogsignals captured by an information sensing array 112, or in the form ofa digitized file 103. For example, such an analysis can includedetection and identification of objects in the captured data. Further,the analysis can include classifying the identified objects, and/or thecaptured data into one or more categories. Other types of analysis canbe additionally or alternatively performed in one or more embodiments ofthe present invention.

Further, at block 304, the ANN 122 is replicated on the informationcapture device 102. The replication includes configuring the ANN-replica204 on the information capture device 102. The ANN-replica 204 isconfigured with the same number of layers, i.e. M. Further, each of thelayers of the ANN-replica 204 are configured with the exact same weightsas the M layers 202 of the ANN 122 of the computer server 106. In one ormore embodiments of the present invention, such a replication includesencrypting the trained weights using the encryption unit 230, andtransmitting the encrypted values to the information capture device 102.The decryption unit 232 decrypts the weight values, which are then usedto configure the ANN-replica 204.

Subsequently, at block 306, the information capture device 102 capturesan analog signal data using the information sensing array 112. Thecaptured data is input to the ANN-replica 204 of the information capturedevice 102 for processing using only k of the M layers of theANN-replica 204, at block 308. The captured data that is input to theANN-replica 204 can be the analog signals captured by the informationsensing array 112, or the corresponding digitized file 103.

Processing the captured data includes selecting the number of layers,i.e., k, to be executed by the information capture device 102, at block310. The layer selector 210 determines the value of k based on one ormore factors associated with the information capture device. In one ormore embodiments of the present invention, the layer selector 210monitors an amount of power consumed to execute each of the layers ofthe ANN-replica 204. Alternatively, or in addition, the layer selector210 has access to power consumption data that indicates amount of powerrequired to execute each of the layers of the ANN-replica 204 by theinformation capture device 102. In one or more embodiments of thepresent invention, the layer selector 210 may further include a powerconsumption budget for the ANN-replica 204. The power consumption budgetcan be a configurable value.

The power consumption budget indicates a maximum amount of power thatthe ANN-replica 204 can consume to analyze the captured data. In one ormore embodiments of the present invention, the power consumption budgetcan be a value that depends on a total amount of power that is availableto the information capture device 102. For example, if the informationcapture device 102 is receiving power from a battery or any other suchlimited power source (not shown), the amount of power that is availablecan depend on a charge-level of the power source. As the charge-levelchanges, the power consumption budget can change. For example, the powerconsumption budget for the ANN-replica 204 can be 100 milliwatts toanalyze the captured data if the charge-level is at least 75% of thecapacity of the power source; the power consumption budget reduces to 80milliwatts when the charge-level drops to 50%; further reduces to 50milliwatts when the charge-level drops to 30%, and so on. It isunderstood that the above example values can vary in one or moreembodiments of the invention. In one or more embodiments of the presentinvention, the relationship between the power consumption budget and thecharge-level can be configurable.

Accordingly, based on the power consumption budget that is determined,and the amount of power required for each of the layers in theANN-replica 204, the layer selector determines that k layers can beexecuted by the information capture device 102 without exceeding thepower consumption budget. In response, the first k layers of theANN-replica 204 are executed by the information capture device 102 (atblock 308).

Because the ANN-replica 204 includes the exact replicas of the M layers202 of the ANN 122, the remaining layers of the ANN 122 (k+1) to M, cantake over the analysis of the captured data. To this end, at block 312,the output of the layer #k from the ANN-replica 204 is transmitted tothe computer server 106 via the network 104. The transmission canfurther include the identity of the layer k, for example, the value ofk.

In one or more embodiments of the present invention, the transmission isencrypted by the encryption unit 220. The output of the layer #k and theidentity of k can be part of a single encrypted transmission in one ormore embodiments of the present invention. Alternatively, separateencrypted transmissions can be performed for the output of the layer #kand the identity of k.

At block 314, the ANN 122 analyzes the captured data by executing thelayers (k+1) to M using the output of the layer #k. Such analysisincludes decrypting, by the decryption unit 222, the informationreceived from the information capture device 102. Further, the layerlocator 212 identifies the layer #k and #k+1 of the ANN 122 andconfigures these layers with the information from the informationcapture device 102 so that the ANN 122 can execute the remainder of theM layers from the layer #k+1.

At block 316, the result of the processing the ANN 122 is output. Theresult can be transmitted to the information capture device 102 in oneor more embodiments of the present invention. Alternatively, or inaddition, the result can be transmitted to another device, such asanother computer server, a database, or any other device.

It should be noted that although FIGS. 1 and 2 depict a singleinformation capture device 102, in one or more embodiments of thepresent invention, multiple information capture devices 102 can becommunicating with the computer server 106. Further, each informationcapture device 102 can have its own power consumption budget,charge-level, and other such varying factors. Accordingly, the number oflayers executed on a first information capture device 102 can vary fromthe number of layers, say k′ (k≠k′), on a second information capturedevice. In response, for the first information capture device, thecomputer server 106 executes a different number of layers (M−k),compared to the number of layers executed for the second informationcapture device (M−k′).

Further, even for a single information capture device 102, the number oflayers k can vary based on the charge-level. For example, the computerserver 106 can execute (M-k) layers of the ANN 122 for a first captureddata that is captured by the information capture device 102 at time t1,when the charge-level is X %; whereas, the computer server 106 executes(M-p) layers of the ANN 122 for a second captured data that is capturedby the information capture device 102 at time t1, when the charge-levelis Y %, p being the number of layers selected by the layer selector 210.

Embodiments of the present invention integrate the neural networkprocess with encryption, by partitioning the neural network and creatinga replica of the neural network on the information capture devices. Thecaptured data is analyzed using a subset of layers of the neural networkat the information capture device, the output of such processing istransmitted to a computer server for further processing using aremainder of the layers of the neural network. The number of layers toexecute at the information capture device is based on one or morefactors, such as power consumption, at the information capture device.The captured data can be used in the form of analog signals or in theform of a digitized file. Further, all of the transmissions, such as theweights for replicating the neural network, the output of the layersexecuted on the information capture device, etc. are encrypted. In thisway, the captured data is not directly transferred through the network,in turn increasing the security of the captured data.

Turning now to FIG. 4, a computer system 400 is generally shown inaccordance with an embodiment. The computer system 400 can be used asthe information capture device 102 and/or the computer server 106 in oneor more embodiments of the present invention. The computer system 400can be an electronic, computer framework comprising and/or employing anynumber and combination of computing devices and networks utilizingvarious communication technologies, as described herein. The computersystem 400 can be easily scalable, extensible, and modular, with theability to change to different services or reconfigure some featuresindependently of others. The computer system 400 may be, for example, aserver, desktop computer, laptop computer, tablet computer, orsmartphone. In some examples, computer system 400 may be a cloudcomputing node. Computer system 400 may be described in the generalcontext of computer system executable instructions, such as programmodules, being executed by a computer system. Generally, program modulesmay include routines, programs, objects, components, logic, datastructures, and so on that perform particular tasks or implementparticular abstract data types. Computer system 400 may be practiced indistributed cloud computing environments where tasks are performed byremote processing devices that are linked through a communicationsnetwork. In a distributed cloud computing environment, program modulesmay be located in both local and remote computer system storage mediaincluding memory storage devices.

As shown in FIG. 4, the computer system 400 has one or more centralprocessing units (CPU(s)) 401 a, 401 b, 401 c, etc. (collectively orgenerically referred to as processor(s) 401). The processors 401 can bea single-core processor, multi-core processor, computing cluster, or anynumber of other configurations. The processors 401, also referred to asprocessing circuits, are coupled via a system bus 402 to a system memory403 and various other components. The system memory 403 can include aread only memory (ROM) 404 and a random-access memory (RAM) 405. The ROM404 is coupled to the system bus 402 and may include a basicinput/output system (BIOS), which controls certain basic functions ofthe computer system 400. The RAM is read-write memory coupled to thesystem bus 402 for use by the processors 401. The system memory 403provides temporary memory space for operations of said instructionsduring operation. The system memory 403 can include random access memory(RAM), read only memory, flash memory, or any other suitable memorysystems.

The computer system 400 comprises an input/output (I/O) adapter 406 anda communications adapter 407 coupled to the system bus 402. The I/Oadapter 406 may be a small computer system interface (SCSI) adapter thatcommunicates with a hard disk 408 and/or any other similar component.The I/O adapter 406 and the hard disk 408 are collectively referred toherein as a mass storage 410.

Software 411 for execution on the computer system 400 may be stored inthe mass storage 410. The mass storage 410 is an example of a tangiblestorage medium readable by the processors 401, where the software 411 isstored as instructions for execution by the processors 401 to cause thecomputer system 400 to operate, such as is described herein below withrespect to the various Figures. Examples of computer program product andthe execution of such instruction is discussed herein in more detail.The communications adapter 407 interconnects the system bus 402 with anetwork 412, which may be an outside network, enabling the computersystem 400 to communicate with other such systems. In one embodiment, aportion of the system memory 403 and the mass storage 410 collectivelystore an operating system, which may be any appropriate operatingsystem, such as the z/OS or AIX operating system from IBM Corporation,to coordinate the functions of the various components shown in FIG. 4.

Additional input/output devices are shown as connected to the system bus402 via a display adapter 415 and an interface adapter 416 and. In oneembodiment, the adapters 406, 407, 415, and 416 may be connected to oneor more I/O buses that are connected to the system bus 402 via anintermediate bus bridge (not shown). A display 419 (e.g., a screen or adisplay monitor) is connected to the system bus 402 by a display adapter415, which may include a graphics controller to improve the performanceof graphics intensive applications and a video controller. A keyboard421, a mouse 422, a speaker 423, etc. can be interconnected to thesystem bus 402 via the interface adapter 416, which may include, forexample, a Super I/O chip integrating multiple device adapters into asingle integrated circuit. Suitable I/O buses for connecting peripheraldevices such as hard disk controllers, network adapters, and graphicsadapters typically include common protocols, such as the PeripheralComponent Interconnect (PCI). Thus, as configured in FIG. 4, thecomputer system 400 includes processing capability in the form of theprocessors 401, and, storage capability including the system memory 403and the mass storage 410, input means such as the keyboard 421 and themouse 422, and output capability including the speaker 423 and thedisplay 419.

In some embodiments, the communications adapter 407 can transmit datausing any suitable interface or protocol, such as the internet smallcomputer system interface, among others. The network 412 may be acellular network, a radio network, a wide area network (WAN), a localarea network (LAN), or the Internet, among others. An external computingdevice may connect to the computer system 400 through the network 412.In some examples, an external computing device may be an externalwebserver or a cloud computing node.

It is to be understood that the block diagram of FIG. 4 is not intendedto indicate that the computer system 400 is to include all of thecomponents shown in FIG. 4. Rather, the computer system 400 can includeany appropriate fewer or additional components not illustrated in FIG. 4(e.g., additional memory components, embedded controllers, modules,additional network interfaces, etc.). Further, the embodiments describedherein with respect to computer system 400 may be implemented with anyappropriate logic, wherein the logic, as referred to herein, can includeany suitable hardware (e.g., a processor, an embedded controller, or anapplication specific integrated circuit, among others), software (e.g.,an application, among others), firmware, or any suitable combination ofhardware, software, and firmware, in various embodiments.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 5, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 5 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 6, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 5) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 6 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 include hardware and software components.Examples of hardware components include mainframes 61; RISC (ReducedInstruction Set Computer) architecture-based servers 62; servers 63;blade servers 64; storage devices 65; and networks and networkingcomponents 66. In some embodiments, software components include networkapplication server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and media processing and classification 96.

Various embodiments of the invention are described herein with referenceto the related drawings. Alternative embodiments of the invention can bedevised without departing from the scope of this invention. Variousconnections and positional relationships (e.g., over, below, adjacent,etc.) are set forth between elements in the following description and inthe drawings. These connections and/or positional relationships, unlessspecified otherwise, can be direct or indirect, and the presentinvention is not intended to be limiting in this respect. Accordingly, acoupling of entities can refer to either a direct or an indirectcoupling, and a positional relationship between entities can be a director indirect positional relationship. Moreover, the various tasks andprocess steps described herein can be incorporated into a morecomprehensive procedure or process having additional steps orfunctionality not described in detail herein.

One or more of the methods described herein can be implemented with anyor a combination of the following technologies, which are each wellknown in the art: a discrete logic circuit(s) having logic gates forimplementing logic functions upon data signals, an application specificintegrated circuit (ASIC) having appropriate combinational logic gates,a programmable gate array(s) (PGA), a field programmable gate array(FPGA), etc.

For the sake of brevity, conventional techniques related to making andusing aspects of the invention may or may not be described in detailherein. In particular, various aspects of computing systems and specificcomputer programs to implement the various technical features describedherein are well known. Accordingly, in the interest of brevity, manyconventional implementation details are only mentioned briefly herein orare omitted entirely without providing the well-known system and/orprocess details.

In some embodiments, various functions or acts can take place at a givenlocation and/or in connection with the operation of one or moreapparatuses or systems. In some embodiments, a portion of a givenfunction or act can be performed at a first device or location, and theremainder of the function or act can be performed at one or moreadditional devices or locations.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used herein, thesingular forms “a”, “an” and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willbe further understood that the terms “comprises” and/or “comprising,”when used in this specification, specify the presence of statedfeatures, integers, steps, operations, elements, and/or components, butdo not preclude the presence or addition of one or more other features,integers, steps, operations, element components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thepresent disclosure has been presented for purposes of illustration anddescription, but is not intended to be exhaustive or limited to the formdisclosed. Many modifications and variations will be apparent to thoseof ordinary skill in the art without departing from the scope and spiritof the disclosure. The embodiments were chosen and described in order tobest explain the principles of the disclosure and the practicalapplication, and to enable others of ordinary skill in the art tounderstand the disclosure for various embodiments with variousmodifications as are suited to the particular use contemplated.

The diagrams depicted herein are illustrative. There can be manyvariations to the diagram or the steps (or operations) described thereinwithout departing from the spirit of the disclosure. For instance, theactions can be performed in a differing order or actions can be added,deleted or modified. Also, the term “coupled” describes having a signalpath between two elements and does not imply a direct connection betweenthe elements with no intervening elements/connections therebetween. Allof these variations are considered a part of the present disclosure.

The following definitions and abbreviations are to be used for theinterpretation of the claims and the specification. As used herein, theterms “comprises,” “comprising,” “includes,” “including,” “has,”“having,” “contains” or “containing,” or any other variation thereof,are intended to cover a non-exclusive inclusion. For example, acomposition, a mixture, process, method, article, or apparatus thatcomprises a list of elements is not necessarily limited to only thoseelements but can include other elements not expressly listed or inherentto such composition, mixture, process, method, article, or apparatus.

Additionally, the term “exemplary” is used herein to mean “serving as anexample, instance or illustration.” Any embodiment or design describedherein as “exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments or designs. The terms “at least one”and “one or more” are understood to include any integer number greaterthan or equal to one, i.e. one, two, three, four, etc. The terms “aplurality” are understood to include any integer number greater than orequal to two, i.e. two, three, four, five, etc. The term “connection”can include both an indirect “connection” and a direct “connection.”

The terms “about,” “substantially,” “approximately,” and variationsthereof, are intended to include the degree of error associated withmeasurement of the particular quantity based upon the equipmentavailable at the time of filing the application. For example, “about”can include a range of ±8% or 5%, or 2% of a given value.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instruction by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdescribed herein.

What is claimed is:
 1. A computer-implemented method for power savingand encryption during analysis of media captured by an informationcapture device using a partitioned neural network, thecomputer-implemented method comprising: replicating, by an informationcapture device, an artificial neural network (ANN) from a computerserver to the information capture device, wherein the ANN on thecomputer server and a replicated ANN on the information capture device,both, include M layers; in response to captured data being input to beprocessed, partially processing, by the information capture device, thecaptured data by executing a first k layers using the replicated ANN,wherein only the k layers are selected to execute on the informationcapture device; and transmitting, by the information capture device, anoutput of the k-th layer to the computer server, which partiallyprocesses the captured data by executing the remainder of the M layersusing the ANN and the output of the k-th layer.
 2. Thecomputer-implemented method of claim 1, further comprising receiving, bythe information capture device, a result of the ANN from the computerserver.
 3. The computer-implemented method of claim 1, wherein the ANNis trained by the computer server prior to the ANN being replicated onthe information capture device.
 4. The computer-implemented method ofclaim 1, wherein the captured data comprises analog signals captured byan information sensing array.
 5. The computer-implemented method ofclaim 1, wherein the captured data comprises a digitized media.
 6. Thecomputer-implemented method of claim 1, wherein replicating the ANN tothe information capture device comprises copying one or more weights ofeach of the M layers of the ANN to corresponding M layers of thereplicated ANN.
 7. The computer-implemented method of claim 6, whereinthe one or more weights are encrypted prior to transmission to theinformation capture device.
 8. The computer-implemented method of claim1, further comprising encrypting the output of the k-th layer prior totransmitting the output to the computer server.
 9. Thecomputer-implemented method of claim 1, further comprising selecting, bythe information capture device, the k layers to execute on theinformation capture device, and transmitting a value of k to thecomputer server.
 10. The computer-implemented method of claim 9, furthercomprising encrypting the value of k prior to transmission to thecomputer server.
 11. A system comprising: a memory; and one or moreprocessors coupled to the memory, wherein the one or more processors areconfigured to perform a method for power saving and encryption duringanalysis of media captured by an information capture device using apartitioned neural network, the method comprising: replicating anartificial neural network (ANN) from a computer server to theinformation capture device, wherein the ANN on the computer server and areplicated ANN on the information capture device, both, include Mlayers; selecting k layers to execute on the information capture device;in response to captured data being input to be processed, partiallyprocessing the captured data by executing the first k layers using thereplicated ANN; and transmitting an output of the k-th layer to thecomputer server, which partially processes the captured data byexecuting the remainder of the M layers using the ANN and the output ofthe k-th layer.
 12. The system of claim 11, wherein the ANN is trainedby the computer server prior to the ANN being replicated on theinformation capture device.
 13. The system of claim 11, wherein thecaptured data comprises analog signals captured by an informationsensing array.
 14. The system of claim 11, wherein the captured datacomprises a digitized media.
 15. The system of claim 11, wherein themethod further comprises encrypting the output of the k-th layer priorto transmitting the output to the computer server.
 16. The system ofclaim 11, wherein the method further comprises encrypting andtransmitting a value of k to the computer server.
 17. A computer programproduct comprising a computer readable storage medium having programinstructions embodied therewith, the program instructions executable byone or more processors to cause the one or more processors to performoperations for power saving and encryption during analysis of mediacaptured by an information capture device using a partitioned neuralnetwork, the operations comprising: replicating, by the informationcapture device, an artificial neural network (ANN) from a computerserver to the information capture device, wherein the ANN on thecomputer server and a replicated ANN on the information capture device,both, include M layers; in response to captured data being input to beprocessed, partially processing, by the information capture device, thecaptured data by executing a first k layers using the replicated ANN,wherein only the k layers are selected to execute on the informationcapture device; and transmitting, by the information capture device, anoutput of the k-th layer to the computer server, which partiallyprocesses the captured data by executing the remainder of the M layersusing the ANN and the output of the k-th layer.
 18. The computer programproduct of claim 17, wherein the captured data comprises analog signalscaptured by an information sensing array.
 19. The computer programproduct of claim 17, wherein the captured data comprises a digitizedmedia.
 20. The computer program product of claim 17, wherein theoperations further comprise encrypting the output of the k-th layerprior to transmitting the output to the computer server.